With growing complexity of embedded software in information processing apparatuses such as control systems, the time spent for verification and debugging of hardware and software is increasing. To address this, it is possible to find hardware and software problems by detecting abnormal behavior (an anomaly point), and to perform verification and debugging of the operation of the information processing apparatus around the detected anomaly point. This makes verification and debugging effective, and therefore reduces the total time spent for the verification and debugging.
Japanese Published Unexamined Patent Application No. 4-68451 describes an invention that stores the names of running tasks at predetermined time intervals and estimates the occupancy rate of the execution time of each task based on the binomial distribution. Patent Document 2 describes an invention that measures the execution time of each task in a computer system executing a plurality of tasks in parallel. More specifically, the invention described in Japanese Published Unexamined Patent Application No. 3-210643 provides an execution time accumulation area for each task, adds the processing time of the task to the execution time accumulation area after the task completion, and, if the task was interrupted by a lower-order task, subtracting the processing time of the lower-order task from the execution time accumulation area.
Japanese Published Unexamined Patent Application No. 2003-242508 describes a method of clustering a plurality of sample points. In this method, a first sum total value of errors between each sample point in a cluster and the centroid of the cluster is summed across a plurality of clusters to compute a second sum total value. As the number of clusters is varied from the maximum toward the minimum, the second sum total value rapidly changes at a certain point. Either number of clusters before or after this point is determined to be an optimal number of clusters. The sample points are clustered into the optimal number of clusters.
Japanese Published Unexamined Patent Application No. 7-234853 describes a cluster classification apparatus that classifies input data. This cluster classification apparatus uses one-dimensional self-organizing feature mapping to generate a map consisting of a prototype group for the input data and classifies the input data according to the map.
For detecting an anomaly point in the operation of an information processing apparatus, the internal state of the information processing apparatus is captured for a long time and exhaustively analyzed. However, this method is inefficient in that a huge amount of internal state is stored. In addition, for detecting an anomaly point in an information processing apparatus requiring real-time operations, for example a controller for a vehicle engine, it is preferable to make as few changes as possible to the embedded software.
Thus, the objective of the present invention is to provide a detecting apparatus, system, program, and detecting method that are capable of solving the above-mentioned problems. The objective is achieved by combinations of features set forth in independent Claims. Dependent Claims define further advantageous embodiments of the present invention.